Sleepr

Problem Space

Sleep and mental health

During my two years as an resident assistant in university, I had first hand experience with the mental health crisis in upper education. My first-year student communities were disconnected from a sense of well-being, and failing to cope with the stressors of university. Along with students from all campuses, they demanded for the university to investigate systemic problems in its network of mental health resources, and propose a solution.

As a staff member I got to interact with students everyday, and began to observe my peers new relationship with sleep. There was a belief that success was dependent on how much sleep we were willing to sacrifice. I learned about the consequences of this idea through my experiences on the job. Mostly by responding to an alarming number of students with suicidal thoughts. This idea about sleep fueled the mental health crisis, leaving us vulnerable to the effects of sleep deprivation at the time and after graduation.

Poor sleep quality is linked to a weakened immune system, and mental health problems like anxiety and depression. The sleep-deprived student/young professional stereotype is a symptom of my generation’s struggles with mental health, and evidence for the need to rekindle our relationship with sleep.

Secondary Research

Sleep deprivation

Firstly, I wanted to understand the scale of sleep deprivation. 

I found that insufficient sleep is an overlooked global public-health epidemic. With the 2019 Phillips Global Sleep Survey reporting that 62% of adults worldwide feel they don’t get enough sleep, averaging 6.8 hours on a weeknight compared to the recommended amount of 8 hours. Respondents gave various reasons for this shortfall, including stress and their sleeping environment, but 37% pointed to their hectic work or school schedule.

Young adults, 18 to 25 years old, report sleeping for less than the recommended 8 to 10 hours for their age group. This presents a long-term problem, since it’s been shown that problems in sleep quality at an early age, predict problems in sleep quality at an older age. But the effects of low-quality sleep are more present on a daily basis. With some researchers suggesting that sleep deprivation is comparable to alcohol intoxication. In fact, it’s easy to experience sleep deprivation if we are not conscious of our sleep debt. Which is when a person grows more tired for every day they don’t get enough sleep. 

The combination of hectic work schedules and sleep debt cycles becomes more problematic when we introduce the effects of the “attention economy.”

As a young adult, social media is an outlet for me to feel connected with the world and culture. However, that connection comes with a sophisticated attempt to monopolize my attention. With the cost being a chunk of time that often goes beyond my initial willingness to pay. Because of this, I end up falling into patterns of behavior that delay the things I want to do. Especially at night, when I’m the most active on my device.

Technology and sleep

The consensus among researchers is that late night technology use is detrimental to our sleep quality for a variety of reasons. For example, the repeated use of a bright screen over 5 days can delay the body clock by 1.5 hours. Causing us to delay our bedtime and sleep in longer.

Late night technology users also report less satisfactory sleep more often, compared to those that don’t use technology before bed. However, a recent anthropological study showed that the reported sleep of a hunter gatherer community with no access to artificial lights, was similar (and sometimes even worse) than their western city-living counterparts. This shows that the presence of technology alone being the main cause of low sleep quality, is an oversimplification of the problem.

Because our sleep is greatly dictated by genetics, I chose to search for an insight within our behaviors with technology in bed. My rationale was that I could better formulate the problem by investigating activities that directly influence our sleep. This, and my previous research would help me define part of our current relationship with technology, and its effect on our sleep.

A behavior problem

As I chose to focus on our behaviors with technology, I found a new design constraint:

How do I use technology to change my user’s behavior, and how they engage with technology itself?

I needed more information before moving forward. But this time, it had to come from my target audience. So I put together a design challenge, and a plan to gather primary research from a group of young adults.

Design Challenge

“How might we…”

With my secondary research, I developed my first design challenge statement:

Interview Insights 

Talking to young adults

To understand how my target user viewed their relationship with technology and sleep, I conducted six 30 minute interviews with young adults ages 18-25. All participants had no previous history of sleep problems, access to digital devices before or in bed, and were currently living in North America.

I prompted participants to self-report their levels of sleep deprivation using the modified BEARS screening medical tool to diagnose sleep problems. Then, I asked them questions about their sleep hygiene, bedtime habits, daily schedule, and the role of technology in their sleep.

In this process I gathered qualitative data. It included the participants attitudes and behaviors towards their current sleep habits, as weel as their use of technology before bed. I analyzed the interview notes for each participant, extracted quotes, and classified them under three categories:

1. Behaviors

2. Motivations

3. Pain points

With this categorization I extracted five themes that provided me with insights into the problem space. After reviewing each theme, I chose two core insights to move forward with in my solution.

Refined Design Challenge

At this stage, I refined my design challenge to reflect the insights I had gathered from my target market.

In this new version, I attempt to reframe the problem space. Firstly, by establishing the use of technology at bedtime as a starting point. And secondly, by stating that my target user wants to fall asleep earlier and improve their sleep quality. Lastly, since most interview participants reported wanting to use their phones at night, it was appropriate to design around it rather than against it.

User Persona & Experience Map

Empathizing

To synthesize my research findings, I developed an user persona profile and user experience map. These tools helped me keep the target user in front of mind, and weigh my design decisions against the research insights.

Meet Lucy

Lucy Chang is 22 years old, and a recent graduate. She currently goes to bed at around midnight with the hopes of falling asleep soon, but ends up using her phone in bed past 1 a.m. in the morning. She finds it entertaining to use her phone in bed, but wants to put a hard stop on it so that she can fall asleep earlier. She wants a quick and easy way to manage her screen time in bed and remember when to put away her phone.

To create my user’s experience map, I explored what a weeknight looks like for Lucy. In the process, I stepped in Lucy’s shoes, broke down her nightly routine, and waged the value for interventions in different areas.

Research expansion

Refining

Through my primary research I narrowed my focus on sleep procrastination and it’s impact on sleep quality. I felt the need for an additional round of secondary research because I lacked information on the nature of sleep procrastination, and the psychology behind it.

Revenge bedtime procrastination

In my research, I found that revenge sleep procrastination is a modern phenomenon that the research community had only recently started to investigate. It’s seen as the decision to sacrifice sleep for leisure time, driven by a desire to “get revenge” at a packed daily schedule with little free time. 

We know it’s sleep procrastination because of three things. There’s a delay in sleep time, there’s no valid reason for staying up late, and the person is aware that this may lead to negative consequences. There are two types of sleep procrastination: bedtime procrastination and while-in-bed procrastination. The former is delaying the act of getting in bed, and the latter is pushing back falling asleep once in bed. Overall, the increase in sleep procrastination is correlated with the increase in the use of mobile devices in bed. 

I decided to tackle while-in-bed procrastination with my intervention because it fitted with the insights I gained through primary research. Also, this type of procrastination aligned well with the narrative of my user persona, and user experience map. Overall, learning about this phenomena helped me clarify the scope of the problem, and fine tune my project goals.

The psychology behind sleep procrastination

I dove in deeper into the psychology behind the problem and found various explanations for the phenomena. For example, some research suggests that people fail to fall asleep on time, even though they are aware of their procrastination. This creates an intention-behavior gap for the individual, which stems partly from low levels of self-regulation or self-control. In fact, the problem I was formulating aligned with this explanation because it shown that “our capacity for self control is at its lowest at the end of the day”, and “daytime demands at work or school reduce the reserves of self control for the evening.”

How is my intervention addressing this?

I decided that my solution would help close this intention-behavior gap. And in order to create a robust product that could do this, I needed to learn how behavioral science addressed and explained these gaps. I found two concepts in the field that helped me formulate the concept for my product:

Present bias

Behavioral science explains that we engage in hyperbolic discounting or present bias. This is an inclination to choose immediate rewards over rewards that come later in the future, even if those immediate rewards are smaller. Research showed that future focus priming, or exposing an user to a stimulus (i.e. word) to influence their response to a second stimulus (i.e. prompting a choice), was an effective way of reducing this cognitive bias.

Getting ready to ideate

This additional research informed my design decisions and allowed me to be more precise in the steps that followed. In summary, I learned:

1. What’s the problem (sleep procrastination)

2. What’s my goal (reduce the intention-behavior gap)

3. How to address it (future focus priming and cost-based commitment devices)

Visual Identity

Creating a brand

With the prototype phase over, I started developing a brand identity for the product and a high fidelity prototype.

Keywords

What identifies my brand

I brainstormed a set of keywords that identified my brand’s personality. Then, I grouped the terms into themes that embodied the brand identity.  Below are each of the themes and keywords:

• TRUST (Reliable, timely, effective, impactful, new)

• SOFT (sleep, calming, relaxing, unwind, rest)

• DARK (bedtime, early, night, evening, dusk)

• RESPONSIBLE (conscious, intentional, mindful, agency, procrastination, control)

Mood Board, Color Palette, and Typography

The look and feel

Using the themes, I searched for images to curate a mood board. This became a source of inspiration and guidance for my design process. For example, I used the mood board to extract colors and curate a color palette, as well as establish my typography system. 

App Name and Wordmark

What’s in a name?

Initially I was leaning towards functional names only, but as I tested different names with users, Sleeper became a favorite among them. It was short, catchy, and it offered room for creativity.

The main idea of the wordmark was to keep it simple, while maintaining a degree of nuance in the design. In the final version, I made subtle changes to the angle of lettering and injected color. Additionally, I removed the last “e” to create rhythm and embrace a modern take that would appeal to my target audience.

High Fidelity Prototype 

Sleepr is born

After going through different color iterations, I finally arrived at the final high-fidelity prototype.

Value Proposition

Why it matters